Multivariate time series anomaly detection is crucial for ensuring reliability in critical applications such as Industrial Internet of Things (IIoT) equipment monitoring and cloud computing platform failure early warning. However, existing methods often struggle when dealing with complex time series characterized by high dimensionality, strong inter-dependencies, and significant variability, particularly in regions with concentrated anomalies. To address these challenges, we propose FIDiff, a diffusion model based on fusion and inference. The framework designs a diffusion model tailored for masked data, which incorporates a time series filling denoiser to enhance the modeling of complex temporal dependencies and mitigate interference from anomalies and noise. Furthermore, a unique multi-step diffusion-based fusion and inference strategy is introduced, which integrates intermediate outputs from models with different diffusion steps and employs a dynamic thresholding mechanism for anomaly scoring, thereby significantly improving detection accuracy and stability. Extensive experiments on six public datasets demonstrate that FIDiff achieves an average improvement of 3.7% in F1-score compared with the best-performing model among 13 state-of-the-art baselines, with consistent performance gains across all datasets, validating its effectiveness and practical value. The code for this project is available at https://github.com/secMTS/FIDiff.git .

The advent of 6G and the Metaverse has created a need for efficient real-time data processing and low-overhead communication. To address this challenge, we propose SemCom-MN, a semantic communication-enhanced Metaverse framework integrating an Edge Service Provider (ESP), Edge Sensing Units (ESUs), and Virtual Service Providers (VSPs). ESUs capture physical-world data, ESP manages semantic information, and VSPs create immersive virtual environments. To improve utility under heterogeneous information and computational requirements, we model semantic information trading as a three-stage Stackelberg game and prove the existence of a Nash equilibrium. Furthermore, to overcome high-dimensional dynamics and slow convergence in semantic trading, we develop a Diffusion Game Algorithm (DGA) combining strategic exploration with a game-theoretic denoising mechanism, achieving robust convergence. Simulation results show DGA increases system utility by 8.49%–33.94%.

Edge detection is a fundamental task in computer vision, essential for various applications such as image segmentation, object detection, and 3D curve reconstruction. Traditional edge detection methods often struggle with semantic relationships, resulting in edges with meaningless parts. Deep learning-based edge detectors, while more effective, rely heavily on pixel-level labels, which are prone to errors and subjective variations. To address these issues, we propose a novel dual-branch attention fusion edge detection model with Canny-assisted supervision (DAF-C). The model integrates the dual attention mechanisms of SimAM and CBAM in parallel, effectively enhancing its ability to recognize complex edge structures. By deeply fusing edge features, it reduces information loss. The designed interactive attention fusion module allows for dynamic recalibration of features, thereby generating edge maps with improved continuity and reduced noise. Furthermore, we introduce Canny-based compensatory supervision during training, which increases label diversity and enables the model to receive more comprehensive and effective supervision. This approach ensures the alignment of semantic comprehension with geometric precision by incorporating Canny edge priors as auxiliary supervision. Extensive experiments on the BSDS500 and Multicue datasets demonstrate that our DAF-C model outperforms existing multi-scale edge detection models, achieving competitive performance and robust edge detection results. The code for this project is available at https://github.com/sfg-oss/daf-c .

This paper investigates the joint trajectory optimization and resource allocation problem in an UAV-Assisted NOMA-MEC System, aiming to minimize the system overhead. First, considering user mobility and Non-Orthogonal Multiple Access (NOMA) technology, we transform UAV trajectory optimization and resource allocation problem as a dynamic coverage location problem. Second, we design a Low-complexity Online Trajectory optimization and Resource allocation Scheme based on Lyapunov and convex optimization (LOTRS) with the goal of minimizing the system overhead. Simulation results show that compared with other benchmark schemes, the proposed LOTRS performs best in terms of system overhead in various scenarios.

Federated learning (FL) enables the training of a global model using clients' local datasets, leveraging their computing resources for efficient machine learning while preserving user privacy. This paper explores FL in wireless networks, focusing on client selection and bandwidth allocation as key factors impacting latency, covert constraint and energy consumption. We propose the per-round energy drift plus cost (PEDPC) algorithm to address this optimization problem from an online perspective. The performance of the PEDPC algorithm is validated through simulations, evaluating latency and energy consumption under both IID and non-IID data distributions.

Zonal occupant level is of great practical interest for building energy saving under normal operations and for fast evacuation under emergency. Though there are many existing sensing systems to estimate this information, the problem is still challenging due to the privacy concerns, the random human movement, and the accumulative error. In this paper, we consider this important problem and focus on infrared beam systems that monitor the zonal arrival and departure events. We make the following contributions. First, a rule (i.e., Rule 1) based on the stay time is developed to reduce the accumulated estimation error in each zone. Second, a rule (i.e., Rule 2) is designed to coordinate the estimation among neighboring zones. A decentralized estimation method is then developed using these two rules. Third, the advantage of this method is demonstrated through simulation results and field tests. We hope this work brings insight to zonal occupant level estimation in buildings in more general situations.

In recent years, applying RFID technology to develop an Indoor Positioning System (IPS) has become a hot research topic. The most prominent advantage of active RFID IPS comes from its unique identification of different objects in indoor environment. However, certain drawbacks of existing RFID IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the location based service, largely limit the applications of active RFID IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID IPS by using cheaper active RFID tags, sensors and reader. In addition, one localization algorithm: integrated Weighted Path Loss (WPL) - Extreme Learning Machine (ELM) which combines the fast estimation of WPL and the high localization accuracy of ELM is proposed. According to the algorithm, an indoor environment is divided into small zones firstly and an ELM model is developed for each zone during the offline phase. During the online phase, the WPL approach is used to determine the zone of the target primarily, then the ELM model of that zone is deployed to provide the final estimated location of the target. Based on our experimental result, this integrated algorithm provides a higher localization efficiency and accuracy than existing approaches.

Robustness is an important requirement on many critical networked infrastructures. One of the most important ways to achieve robustness is to keep an appropriate level of redundancy by adding redundant resources for the critical networked infrastructures. Considering wireless with good reconfigurabilities, we add wireless communication capacities to selected nodes of a wired networked system in this paper, where we firstly focus on adding minimum wireless communication capacities to achieve biconnectivity requirements, secondly optimize the wireless capacities installation that maximizes the efficiency of reconfiguration, and thirdly propose an approach of topology reconfiguration with wireless communication to keep system connectivity if there are node failures. To resolve the computational complexity issue in searching for the optimal node combination to add wireless capacities, we propose the concept of the equivalent network that reduces the system topology as a simplified acyclic network. The topology optimization is under a metric of reconfiguration distance which guarantees the maximum of the shortest distances between any one node and that in the set of reconfigurable nodes to be minimal such that the efficiency of topology reconfiguration with node failure is the highest. The performances of the methods presented in the paper are demonstrated through numerical experiments and testing.